Determining the duration and length of a news event’s impact on a company’s performance remains elusive for financial analysts. The complexity arises from the fact that the effects of these news articles are influenced by various extraneous factors and can change over time. As a result, in this work, we investigate our ability to predict 1) the duration (length) of a news event’s impact, and 2) level of impact on companies. The datasets used in this study are provided as part of the Multi-Lingual ESG Impact Duration Inference (ML-ESG-3) shared task. To handle the data scarcity, we explored data augmentation techniques to augment our training data. To address each of the research objectives stated above, we employ an ensemble approach combining transformer model, a variant of Convolutional Neural Networks (CNNs), specifically the KimCNN model and contextual embeddings. The model’s performance is assessed across a multilingual dataset encompassing English, French, Japanese, and Korean news articles. For the first task of determining impact duration, our model ranked in first, fifth, seventh, and eight place for Japanese, French, Korean and English texts respectively (with respective macro F1 scores of 0.256, 0.458, 0.552, 0.441). For the second task of assessing impact level, our model ranked in sixth, and eight place for French and English texts, respectively (with respective macro F1 scores of 0.488 and 0.550).